Control loop performance monitoring based on weighted permutation entropy and control charts

Wu, Ping; Guo, Lingling; Duan, Yiyong; Zhou, Wensheng; He, Guojun

doi:10.1002/cjce.23366

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…Wu et al [26] employed weighted permutation entropy (WPE) as an index to assess control loop performance, and used a historical database with Shewhart and EWMA control charts to define the control limits. Permutation entropy was suggested as a measure to characterize the dynamic complexity of the closed loop output.…”

Section: Related Workmentioning

confidence: 99%

Impact of control loop performance on energy use, air quality, and thermal comfort in building systems with advanced sequences of operation

Haleem

Pavlak

Bahnfleth

2021

Automation in Construction

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Section: Related Workmentioning

confidence: 99%

Impact of control loop performance on energy use, air quality, and thermal comfort in building systems with advanced sequences of operation

Haleem

Pavlak

Bahnfleth

2021

Automation in Construction

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“…WPE has a better anti-noise and anti-interference ability, its algorithm is simple, and its operability is strong. It can effectively amplify the small changes of the time series, and achieve good application results in mechanical fault diagnosis, medicine, and other fields [ 28 , 29 ]. However, WPE is only based on a single-scale measure.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

Song

Wang

Zhao

et al. 2022

Entropy

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In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

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“…To this end, due to the preamble weights, the weighted permutation entropy (WPE) [ 20 ] is further critical to the amplitude-coded information in the signal and has outperformed the PE in combating the distortion caused by noise. To the best of the authors’ knowledge, WPE has been widely used in uncertainty measurements in many fields [ 21 , 22 ], but is rarely used in the SRN feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Esmaiel

Xie²,

Qasem

et al. 2021

Sensors

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Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

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Control loop performance monitoring based on weighted permutation entropy and control charts

Cited by 6 publications

References 25 publications

Impact of control loop performance on energy use, air quality, and thermal comfort in building systems with advanced sequences of operation

Impact of control loop performance on energy use, air quality, and thermal comfort in building systems with advanced sequences of operation

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

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